A transmission control scheme for media access in sensor networks
Proceedings of the 7th annual international conference on Mobile computing and networking
A two-tier data dissemination model for large-scale wireless sensor networks
Proceedings of the 8th annual international conference on Mobile computing and networking
HEED: A Hybrid, Energy-Efficient, Distributed Clustering Approach for Ad Hoc Sensor Networks
IEEE Transactions on Mobile Computing
Manifold learning visualization of network traffic data
Proceedings of the 2005 ACM SIGCOMM workshop on Mining network data
A survey of communication/networking in Smart Grids
Future Generation Computer Systems
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In this paper, we propose a new network management framework for large-scale randomly-deployed sensor networks, called Energy Map, which explores the inherent relationships between the energy consumption and the sensor operation. Through nonlinear manifold learning algorithms, we are able to: 1) visualise the residual energy level of each sensor in a largescale network 2) infer the sensor locations and the current network topology through mining the collected residual energy data in a randomly-deployed sensor network 3) explore the inherent relation between sensor operation and energy consumption to find the dynamic patterns from a large volume of sensor network data for further network design, such as which set of sensors in a network will be the best candidates to be the future cluster heads, which is usually very important to develop a good sensor network protocol stack such as clustering algorithms and routing protocols.